Abstract

Discusses a technique for realising self-organising feature maps which exploit the properties of {0,1}n space. Working within the digital domain permits the generation of large fast networks using conventional computing machinery. Though the method exploits some of the methods of conventional N-tuple recognisers, such as WISARD, it differs in that it is an unsupervised learning process and that the output map is topologically organised. The authors concentrate on various extensions to the technique, including improved output map generation, reconstruction of corrupted input data by oversampling, and grey-scale input mapping; together with system realisation in hardware

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

Discusses a technique for realising self-organising feature maps which exploit the properties of {0,1}n space. Working within the digital domain permits the generation of large fast networks using conventional computing machinery. Though the method exploits some of the methods of conventional N-tuple recognisers, such as WISARD, it differs in that it is an unsupervised learning process and that the output map is topologically organised. The authors concentrate on various extensions to the technique, including improved output map generation, reconstruction of corrupted input data by oversampling, and grey-scale input mapping; together with system realisation in hardware